Model-based metrology using images

ABSTRACT

Methods and systems for combining information present in measured images of semiconductor wafers with additional measurements of particular structures within the measured images are presented herein. In one aspect, an image-based signal response metrology (SRM) model is trained based on measured images and corresponding reference measurements of particular structures within each image. The trained, image-based SRM model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers. In another aspect, a measurement signal synthesis model is trained based on measured images and corresponding measurement signals generated by measurements of particular structures within each image by a non-imaging measurement technique. Images collected from other wafers are transformed into synthetic measurement signals associated with the non-imaging measurement technique and a model-based measurement is employed to estimate values of parameters of interest based on the synthetic signals.

CROSS REFERENCE TO RELATED APPLICATION

The present application for patent claims priority under 35 U.S.C. § 119from U.S. provisional patent application Ser. No. 62/212,113, entitled“Model-Based Metrology Using Images,” filed Aug. 31, 2015, the subjectmatter of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The described embodiments relate to metrology systems and methods, andmore particularly to methods and systems for improved model-basedmeasurements.

BACKGROUND INFORMATION

Semiconductor devices such as logic and memory devices are typicallyfabricated by a sequence of processing steps applied to a specimen. Thevarious features and multiple structural levels of the semiconductordevices are formed by these processing steps. For example, lithographyamong others is one semiconductor fabrication process that involvesgenerating a pattern on a semiconductor wafer. Additional examples ofsemiconductor fabrication processes include, but are not limited to,chemical-mechanical polishing, etch, deposition, and ion implantation.Multiple semiconductor devices may be fabricated on a singlesemiconductor wafer and then separated into individual semiconductordevices.

Metrology processes are used at various steps during a semiconductormanufacturing process to detect defects on wafers to promote higheryield. Optical metrology techniques offer the potential for highthroughput without the risk of sample destruction. A number of opticalmetrology based techniques including scatterometry and reflectometryimplementations and associated analysis algorithms are commonly used tocharacterize critical dimensions, film thicknesses, composition, overlayand other parameters of nanoscale structures. Non-imaging, model-basedoptical metrology techniques generally acquire measurement signalssequentially and usually from metrology targets that are sparselylocated on a field area of a semiconductor wafer. Although non-imaging,model-based optical metrology techniques offer high precisionmeasurement capability, the number of locations that can be measured fora given wafer throughput requirement is limited.

In contrast, imaging based measurement systems collect large numbers ofsignals in parallel. Thus, the wafer area that can be characterized byimaging-based measurements for a given wafer throughput requirement ismuch larger compared to model-based optical metrology techniques.Unfortunately, at this time, imaging-based measurements lack sufficientresolution to directly measure complex three dimensional structures thatare commonly manufactured today.

Image based measurements typically involve the recognition of specifictarget features (e.g., line segments, boxes, etc.) in an image andparameters of interest are calculated based on these features.Typically, the specialized target structures are specific to the imageprocessing algorithm. For example, the line segments associated with anoverlay target (e.g., box-in-box target, frame-in-frame target, advancedimaging metrology (AIM) target) are specifically designed to comply withthe specifics of the algorithm. For this reason, traditional image basedmetrology algorithms cannot perform reliably with arbitrary targets ordevice structures.

In semiconductor manufacture, and patterning processes in particular,process control is enabled by performing metrology on specific dedicatedstructures. These dedicated structures may be located in the scribelines between dies, or within the die itself. The measurement ofdedicated metrology structures by traditional scatterometry basedmetrology techniques is time consuming.

Future metrology applications present challenges for image basedmetrology due to increasingly small resolution requirements and theincreasingly high value of wafer area. Thus, methods and systems forimproved image-based measurements are desired.

SUMMARY

Methods and systems for combining the information content present inmeasured images of semiconductor wafers with additional measurements ofparticular structures within the measured images to quickly andaccurately estimate structural parameters of interest are presentedherein.

In one aspect, the high information content present in measured imagesis transformed into estimated values of structural parameters ofinterest. An image-based signal response metrology (SRM) model istrained based on measured, image-based training data (e.g., imagescollected from a Design of Experiments (DOE) wafer) and correspondingreference measurement data. The trained, image-based measurement modelis then used to calculate values of one or more parameters of interestdirectly from measured image data collected from other wafers. Thetrained, image-based SRM models described herein receive image datadirectly as input and provide estimates of values of one or moreparameters of interest as output. By streamlining the measurementprocess, the predictive results are improved along with a reduction incomputation and user time.

By using only raw image data to create the image-based measurementmodel, as described herein, the errors and approximations associatedwith traditional image based metrology methods are reduced. In addition,the image-based measurement model is not sensitive to systematic errors,asymmetries, etc. because the image-based measurement model is trainedbased on image data collected from a particular metrology system andused to perform measurements based on images collected from the samemetrology system.

In another aspect, measured images are transformed into syntheticnon-imaging based measurement signals associated with a model-basedmeasurement technique at one or more locations a field. The model-basedmeasurement technique is employed to estimate values of structuralparameters of interest based on the synthetic signals. A measurementsignal synthesis model is trained based on measured, image-basedtraining data (e.g., images collected from a Design of Experiments (DOE)wafer) and corresponding non-imaging measurement data. In a furtheraspect, synthetic signals are generated for multiple structures indifferent locations in each imaged field. In some examples, performingmodel based measurements based on the synthetic signals is significantlyfaster than acquiring actual measurement data at each differentlocation.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not limiting in any way. Other aspects,inventive features, and advantages of the devices and/or processesdescribed herein will become apparent in the non-limiting detaileddescription set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system 100 for performing measurements ofparameters of interest in accordance with the exemplary methodspresented herein.

FIG. 2 is a flowchart illustrative of a method 200 of training an imagebased SRM model as described herein.

FIG. 3 is a flowchart illustrative of a method 210 of performingmeasurements of a structure using the trained SRM model described withreference to method 400.

FIG. 4 depicts a design of experiments wafer 1600 having a grid ofmeasurement sites including structures that exhibit known variations ofone or more parameters of interest.

FIG. 5 depicts illustrative images 162-164 of different measurementsites of wafer 160.

FIG. 6 illustrates a grid of pixels 165 associated with image 162.

FIG. 7 depicts different pixel locations selected for model training andmeasurement in accordance with method the methods described herein.

FIG. 8 depicts a vector 176 of measured intensity values sampled at thepixel locations illustrated in FIG. 7.

FIG. 9 is a flowchart illustrative of a method 220 of training ameasurement signal synthesis model as described herein.

FIG. 10 is a flowchart illustrative of a method 230 of performingmeasurements of a structure using the measurement signal synthesis modeldescribed with reference to method 220.

DETAILED DESCRIPTION

Reference will now be made in detail to background examples and someembodiments of the invention, examples of which are illustrated in theaccompanying drawings.

Methods and systems for combining the information content present inmeasured images of semiconductor wafers with additional measurements ofparticular structures within the measured images to quickly andaccurately estimate structural parameters of interest are presentedherein.

FIG. 1 illustrates a system 100 for measuring characteristics of aspecimen in accordance with the exemplary methods presented herein. Asshown in FIG. 1, the system 100 may be used to perform imaging andnon-imaging measurements of one or more structures formed on a specimen107. In this aspect, the system 100 may configured as a beam profilereflectometer (BPR), a field imaging system, and a spectroscopicreflectometer (SR). Alternatively, system 100 may be configured as aBPR, a spectroscopic ellipsometer (SE), and a field imaging system.System 100 includes a high numerical aperture (NA) objective lens (e.g.,NA>0.9) and at least one collection beam splitter 110 to generate anoptical path to the pupil detector 117 and another optical path to thefield detector 113 or 114. The field detector and pupil detector acquirefield signals 121 or 122 and pupil signals 123, respectively, fromspecimen 107. Field images or pupil images are processed to estimate oneor more structural or process parameter values.

As depicted in FIG. 1, system 100 includes an illumination source 101that generates an amount of illumination light 119. In some embodiments,illumination source 101 is a broadband illumination source such as axenon lamp, a laser driven light source, a multiple wavelength laser, asupercontinuum laser, etc. In some other embodiments, illuminationsource 101 includes a narrowband light source such as a singlewavelength laser, a tunable narrowband laser, etc. In some embodiments,illumination source 101 includes a combination of broadband andnarrowband illumination sources. In some embodiments, optical filtersare included to select one or more illumination wavelength(s) andcorresponding wavelength range(s).

As depicted in FIG. 1, illumination light 119 passes throughillumination optics 102. Illumination optics 102 focus and collimate theillumination light. Illumination optics 102 include lens components,mirror components, or a combination of both. Illumination light passesthrough one or more selectable illumination apertures 104 beforereaching illumination beam splitter 105. In some embodiments, theselectable illumination apertures 104 include a set of illuminationfield stops and a set of illumination pupil stops. The illuminationfield stops are configured to select the illumination spot sizeprojected onto specimen 107. The illumination pupil stops are configuredto select the illumination pupil projected onto specimen 107. Theillumination field stops and pupil stops operate in conjunction withother illumination optics components (e.g., illumination optics 102 andobjective 106) to achieve an illumination NA tuned for optimal lightthroughput, illumination field of view, and pupil on the surface ofspecimen 107. The aperture(s) of the selectable illumination apertures104 may be formed by any suitable device including, but not limited to amechanical pin-hole, a spatial light modulator (SLM), an apodizer, andany other beam forming and controlling component or sub-system.

Illumination beam splitter 105 directs a portion of the collimatedillumination light to objective 106 and directs another portion of thecollimated illumination light to intensity monitor 108. In someembodiments, intensity monitor 108 is communicatively coupled tocomputing system 130 and provides an indication of the overallillumination intensity, the illumination intensity profile, or both, tocomputing system 130. Objective 106 directs illumination light to thesurface of specimen 107 over a broad range of angles of incidence. Lightreflected, diffracted, and scattered from the surface of specimen 107 iscollected by objective 106 and passes through collection beam splitter110. A portion of the collected light is directed through a fielddetection path, while another portion of the collected light is directedthrough a pupil detection path. Illumination beam splitter 105 andcollection beam splitter 110 may include any suitable beam splittingelement including, but not limited to, a cubic beam splitter, a metalliccoating plate, a dichroic optical coating plate, or other beam splittingmechanism.

The field detection path includes a selectable field collection aperture111, focusing field optics 112, and at least one field detector. In someembodiments, the selectable field collection aperture 111 includes a setof field stops to select signals for projection onto field signaldetectors 113 or 114. In some examples, higher order field signals areselected for projection onto field signal detectors 113 or 114. Theaperture(s) of the selectable field collection aperture 111 may beformed by any suitable device including, but not limited to a mechanicalpin-hole, a spatial light modulator (SLM), an apodizer, and any otherbeam forming and controlling component or sub-system.

In the embodiment depicted in FIG. 1, system 100 includes a fieldimaging detector 114 and a spectroscopic field detector 113. A flip-inmirror mechanism 120 is selectively located in the field detection pathbased on a command signal (not shown) received from computing system130. In one configuration, flip-in mirror mechanism 120 is located inthe field detection path and the collected light is directed to fieldimaging detector 114. In another configuration, flip-in mirror mechanism120 is located outside the field detection path and the collected lightis directed toward spectroscopic field detector 113. In this manner,system 100 is configured to perform either image-based or spectroscopicbased field measurements. In one embodiment, field imaging detector 114images a portion of the wafer surface illuminated by the illuminationsource onto the detector. Field imaging detector 114 may be CCD camera,CMOS camera, array detector, etc.

The pupil detection path includes a selectable pupil collection aperture118, a selectable narrow band pass filter 115, and pupil relay optics116 that direct the collected light to pupil detector 117. In someembodiments, the selectable pupil collection aperture 118 includes a setof field stops to select signals for projection onto pupil signaldetector 117. In some examples, higher order pupil signals are selectedfor projection onto pupil signal detector 117. The aperture(s) of theselectable pupil collection aperture 118 may be formed by any suitabledevice including, but not limited to a mechanical pin-hole, a spatiallight modulator (SLM), an apodizer, and any other beam forming andcontrolling component or sub-system.

In the depicted embodiment, pupil detector 117 is an imaging detector.However, in some other embodiments, pupil detector 117 is aspectroscopic detector. In general, the pupil detection path may includeone or more pupil detectors configured to collect pupil datasimultaneously or sequentially.

As described herein, the pupil images detected by pupil imaging detector117 or the field images detected by field imaging detector 114 may beused to measurement parameters of interest directly based on an imagebased SRM model, or indirectly, based on a measurement signal synthesismodel, as described herein. In one embodiment, spectroscopic fielddetector 113 is a spectrometer. By way of non-limiting example, thedetected spectra may also be used for measurement of parameters ofinterest. Exemplary parameters of interest include any of a criticaldimension (CD) parameter, an overlay parameter, a focus parameter, adose parameter, a structure asymmetry parameter, a structure roughnessparameter, a directed self assembly (DSA) pattern uniformity parameter,a pitch walk parameter, etc.

In the embodiment depicted in FIG. 1, system 100 includes a polarizer103 in the illumination path and an analyzer 109 in the collection path.Depending on whether polarizer 103 is rotating or not, system 100 may beconfigured to perform spectroscopic reflectometry (SR) measurements orspectroscopic ellipsometry (SE) measurements. In this manner, system 100may be selectively configured to perform SR or SE measurements.

In addition, system 100 includes a measurement device (e.g., encoder125) configured to measure the position of specimen 107 relative to theoptical system in the direction perpendicular to the surface of specimen107 (i.e., z-direction depicted in coordinate frame 126). In thismanner, encoder 125 provides an indication of the focus position ofspecimen 107 relative to the optical system. Pupil signals 123 and fieldsignals 121 or 122 can be collected along with an indication of focusposition 124 for analysis by computing system 130. Based on an estimateof focus position, computing system 130 communicates command signals toeither a wafer positioning system (not shown) or an optical positioningsystem (not shown) to adjust the focus position of specimen 107 relativeto the optical system. In this manner, the focus position of specimen107 is monitored and adjusted during image acquisition. In some otherexamples, image data is collected while moving the focus position ofspecimen 107 incrementally or continuously in the z-direction.

Traditionally, model based measurements of parameters of interest areperformed based on non-imaging measurement data (e.g., spectral datacollected by detector 113). For example, model based CD measurementinvolves a CD measurement model including a parameterization of ametrology target in terms of the CD parameter of interest. In addition,the measurement model includes a parameterization of the measurementtool itself (e.g., wavelengths, angles of incidence, polarizationangles, etc.). In addition, simulation approximations (e.g., slabbing,Rigorous Coupled Wave Analysis (RCWA), etc.) are carefully performed toavoid introducing excessively large errors. Discretization and RCWAparameters are defined.

Machine parameters (P machine) are parameters used to characterize themetrology tool itself. Exemplary machine parameters include angle ofincidence (AOI), analyzer angle (A0), polarizer angle (P0), illuminationwavelength, numerical aperture (NA), etc. Specimen parameters(P_(specimen)) are parameters used to characterize the geometric andmaterial properties of the specimen.

For measurement purposes, the machine parameters of the multi-targetmodel are treated as known, fixed parameters and the specimen parametersof the measurement model, or a subset of specimen parameters, aretreated as unknown, floating parameters. The floating parameters areresolved by a fitting process (e.g., regression, library matching, etc.)that produces the best fit between theoretical predictions and measureddata. The unknown specimen parameters, P_(specimen), are varied and themodel output values are calculated until a set of specimen parametervalues are determined that results in a close match between the modeloutput values and the measured values. Performing measurements in thismanner is computationally expensive.

In one aspect, the high information content present in measured imagesis transformed into estimated values of structural parameters ofinterest. An image-based signal response metrology (SRM) model istrained based on measured, image-based training data (e.g., imagescollected from a Design of Experiments (DOE) wafer) and correspondingreference measurement data. The trained, image-based measurement modelis then used to calculate values of one or more parameters of interestdirectly from measured image data collected from other wafers. Thetrained, image-based SRM models described herein receive image datadirectly as input and provide estimates of values of one or moreparameters of interest as output. By streamlining the measurementprocess, the predictive results are improved along with a reduction incomputation and user time.

By using only raw image data to create the image-based measurementmodel, as described herein, the errors and approximations associatedwith traditional image based metrology methods are reduced. In addition,the image-based measurement model is not sensitive to systematic errors,asymmetries, etc. because the image-based measurement model is trainedbased on image data collected from a particular metrology system andused to perform measurements based on images collected from the samemetrology system.

In some examples, the image-based SRM model can be created in less thanan hour. In addition, by employing a simplified model, measurement timeis reduced compared to existing image based metrology methods.Additional modeling details are described in U.S. Patent Publication No.2014/0297211 and U.S. Patent Publication No. 2014/0316730, the subjectmatter of each are incorporated herein by reference in their entirety.

In general, the methods and systems described herein analyze each imageas a whole. Instead of recognizing individual features in the image,each pixel is considered as an individual signal containing informationabout (or sensitive to) structural parameters, process parameters,dispersion parameters, etc.

FIG. 2 illustrates a method 200 suitable for implementation by ameasurement system such as measurement system 100 illustrated in FIG. 1of the present invention. In one aspect, it is recognized that dataprocessing blocks of method 200 may be carried out via a pre-programmedalgorithm executed by one or more processors of computing system 130, orany other general purpose computing system. It is recognized herein thatthe particular structural aspects of measurement system 100 do notrepresent limitations and should be interpreted as illustrative only.

In block 201, a plurality of Design Of Experiments (DOE) measurementsites are illuminated by an illumination source. The DOE measurementsites are located at a number of different fields located on one or moreDOE wafers. Each DOE measurement site includes an instance of at leastone structure characterized by at least one parameter of interest. Thestructure can be a dedicated metrology target, device structure, gratingstructure, etc.

The parameters of interest include one or more process parameters,structural parameters, dispersion parameters, or layout parameters. Eachof the measurement sites includes the same nominal structures at thesame nominal locations within each of the measurement sites. In oneexample, a measurement site encompasses a field area of a semiconductorwafer that is repeatedly constructed across the wafer surface. In someexamples, a measurement site encompasses a die area that is repeatedlyconstructed across the wafer surface. Although, each measurement sitenominally includes the same structures, in reality, and for purposes ofmodel training, each measurement site includes variations of variousparameters (e.g., CD, sidewall angle, height, overlay, etc.).

For purposes of model training, variations of the parameter(s) ofinterest are organized in a Design of Experiments (DOE) pattern on thesurface of a semiconductor wafer (e.g., DOE wafer). In this manner, themeasurement sites at different locations on the wafer surface correspondto different values of the parameter(s) of interest. In one example, theDOE pattern is a focus exposure matrix (FEM) pattern. Typically, a DOEwafer exhibiting an FEM pattern includes a grid pattern of measurementsites. In one grid direction (e.g., the x-direction), the focus isvaried while the exposure is held constant. In the orthogonal griddirection (e.g., the y-direction), the exposure is varied while thefocus is held constant. In this manner, image data collected from theDOE wafer includes data associated with variations in focus andexposure. FIG. 4 depicts a DOE wafer 160 having a grid of measurementsites (e.g., measurement site 161) including structures that exhibitvariations in the parameter(s) of interest (e.g., focus and exposure).The focus varies as a function of location on the DOE wafer 160 in thex-direction. The exposure varies as a function of location on the DOEwafer 160 in the y-direction.

In some embodiments, the images include device areas. Each pixel of aparticular image of a measurement site represents the intensity of thecollected light under specific illumination and collection conditions,wavelengths, polarization, etc. FIG. 5 depicts images 162-164 ofdifferent measurement sites of wafer 160. Each image represents anaerial view of the device structures within a measurement site. Themeasurement site is identified by its X and Y coordinates.

In some other embodiments, the images include specific targets designedto facilitate image-based measurement of the parameter(s) of interest. Aspecially designed target may be employed to improve devicerepresentation, maximize sensitivity to the parameter(s) of interest(focus, dose, CD), and reduce correlation to process variation.

In the aforementioned example, the image data is associated with a DOEwafer processed with variations in focus and exposure (i.e., dose).However, in general, image data associated with any variation of processparameters, structural parameter, dispersion, etc., may be contemplated.The images of the DOE wafer should exhibit ranges of the parameter(s) ofinterest.

In block 202, light imaged from each of the plurality of DOE measurementsites is detected in response to the illuminating of each of theplurality of DOE measurement sites. In one example, field imagingdetector 114 depicted in FIG. 1 detects light imaged from the surface ofwafer 107 at each DOE measurement site. In another example, pupilimaging detector 117 detects light imaged from the pupil of objective106 at each DOE measurement site.

In block 203, an image of each of the plurality of DOE measurement sitesis generated. In one example, field imaging detector 114 generates animage of each of the DOE measurement sites and communicates signals 122indicative of each generated image to computing system 130. In anotherexample, pupil imaging detector 117 generates a pupil image of each ofthe DOE measurement sites and communicates signals indicative of eachgenerated pupil image to computing system 130.

In some examples, a single image of each measurement site is generated.In these examples, each image of each measurement site includes a singlemeasurement signal value associated with each image pixel. In someexamples, the single measurement value is a reflectance at the locationof each pixel measured by an imaging reflectometer at a particular setof measurement system settings (e.g., wavelength, polarization, angle ofincidence, azimuth angle, etc.).

In some other examples, multiple images of each measurement site aregenerated. Each of the images of each measurement site includes a singlemeasurement signal value associated with each pixel. Thus, multiplemeasurement signal values are measured for each pixel. In general, eachof the images of each measurement site is measured either by the samemeasurement system at different settings (e.g., wavelength,polarization, angle of incidence, azimuth angle, etc.), a differentmeasurement technique, or a combination thereof. In this manner, adiverse set of measurement data may be assembled for each pixel of eachmeasurement site. In general, image data can be collected from anyimaging based system such as an optical imaging system, a microscope, ascanning electron microscope, a tunneling electron microscope, or otherimage forming systems.

In block 204, a reference measurement value of the at least oneparameter of interest is estimated at each of the plurality of DOEmeasurement sites by a trusted, reference metrology system. Referencemeasurements are performed by a reference measurement system, orcombination of reference measurement systems based on any suitablemetrology technique, or combination of metrology techniques. By way ofnon-limiting example, any of a scanning electron microscope, an opticalbased measurement system, an x-ray based measurement system, a tunnelingelectron microscopy system, and an atomic force microscopy system may beemployed to perform reference measurements of DOE measurement sites.

As depicted in FIG. 1, in one example, reference measurements 151 ofparameters of interest at each DOE measurement site are communicatedfrom a reference measurement source 150 to computing system 130.

In another example, depicted in FIG. 1, spectroscopic field detector 113generates measurement signals 121 indicative of light collected from oneor more structures characterized by each parameter of interest withineach measurement site. In this example, the measurement signals arespectroscopic scatterometry signals. Computing system 130 performs amodel based measurement (e.g., optical critical dimension measurement)to estimate the value of each parameter of interest at each measurementsite based on the detected measurement signals 121.

In an optional block (not shown), each of the images is aligned with acommon reference location of each measurement site. In this manner, anyparticular pixel from each image corresponds to the same location oneach imaged measurement site. In one example, the collected images arealigned such that they match the first image of the set. FIG. 6illustrates a grid of pixels 165 associated with image 162. In someexamples, the measurement system operates at high precision andadditional image alignment is not necessary. In this sense, imagealignment is optional.

In another optional block (not shown), each of the images is filtered byone or more image filters. Image filters may be employed for noisereduction, contrast enhancement, etc. In one example, image filters maybe employed to reduce edge effects by detecting edges and removing ormasking the edges and proximate regions. In this manner, subsequentimage samples are taken from relatively homogenous device regions. Theimage filters employed may be selected by a user or by an automaticprocedure. The number of different image filters and the parametersassociated with each selected filter are chosen to improve the finalmeasurement result without undue computational burden. Although, the useof image based filters may be advantageous, in general, it is notnecessary. In this sense, image filtering is optional.

Each image of each measurement site may include millions of pixels, andonly a small number of those pixels have any correlation with theparameters of interest. In another optional block (not shown), a subsetof the pixels associated with each of the first plurality of images isselected for model training and measurement. The measurement signalvalues associated with the same selected pixels of each of the firstplurality of images are used for model training and measurement. In manyexamples, this is desirable to minimize computational effort.

FIG. 7 depicts two different groups of pixels at different locationsselected for model training and measurement. In the depicted example,pixel groups 170, 172, and 174 correspond to the same location on images162, 163, and 164, respectively. Similarly, pixel groups 171, 173, and175 correspond to the same location on images 162, 163, and 164,respectively. The measurement signals associated with each of thesepixels are used for model training and measurement. FIG. 8 depicts avector 176 of measured intensity (e.g., reflectance) values sampled atthe pixel locations illustrated in FIG. 7. This sampled image data isused for model training and measurement. In the example depicted in FIG.8, ¹I_((I1,J1)) is the intensity value associated with pixel group 170of image 162, ²I_((I1,J1)) is the intensity value associated with pixelgroup 172 of image 163, and ^(N)I_((I1,J1)) is the intensity valueassociated with pixel group 174 of image 164. Similarly, ¹I_((I2,J2)) isthe intensity value associated with pixel group 171 of image 162,²I_((I2,J2)) is the intensity value associated with pixel group 173 ofimage 163, and ^(N)I_((I2,J2)) is the intensity value associated withpixel group 175 of image 164. In this manner, vector 176 includesintensity measurement signals from pixels groups at the same location ofeach imaged measurement site.

In some examples, pixels or groups of pixels are selected for theirproximity to structures characterized by the parameters of interest. Inone example, selected pixels are associated with an area around astructure of interest that is five to ten times as large as thestructure of interest. In other examples, pixel locations are selectedrandomly. In some other examples, the pixel locations are selected basedon their measurement sensitivity. In one example, the variance ofmeasurement signal values associated with each pixel location iscalculated from the ensemble of images. The variance associated witheach pixel location is a metric that characterizes the measurementsensitivity at each corresponding pixel location. Pixel locations withrelatively high variance offer higher measurement sensitivity and areselected for further analysis. Pixel locations with relatively lowvariance offer lower measurement sensitivity and are discarded. In someexamples, a predetermined threshold value for variance is selected, andpixel locations with a variance that exceeds the predetermined thresholdvalue are selected for model training and measurement. In this manner,only the most sensitive locations are sampled. In some examples, all ofthe pixels associated with each of the first plurality of images areselected for model training and measurement. In this sense, pixelselection is optional.

In another optional block (not shown), a feature extraction model isdetermined based on the selected image data. The feature extractionmodel reduces a dimension of the image data. A feature extraction modelmaps the original signals to a new reduced set of signals. Thetransformation is determined based on the variations in the parameter(s)of interest in the selected images. Each pixel of each image is treatedas an original signal that changes within the process range fordifferent images. The feature extraction model may be applied to all ofthe image pixels, or a subset of image pixels. In some examples, thepixels subject to analysis by the feature extraction model are chosenrandomly. In some other examples, the pixels subject to analysis by thefeature extraction model are chosen due to their relatively highsensitivity to changes in the parameter(s) of interest. For example,pixels that are not sensitive to changes in the parameter(s) of interestmay be ignored.

By way of non-limiting example, the feature extraction model may aprincipal component analysis (PCA) model, a kernel PCA model, anon-linear PCA model, an independent component analysis (ICA) model orother dimensionality reduction methods using dictionaries, a discretecosine transform (DCT) model, fast fourier transform (FFT) model, awavelet model, etc.

In a typical design of experiments, the locations on the wafer areprogrammed to have specific geometric and process parameter values(e.g., focus, dose, overlay, CD, sidewall angle, height etc.). Hence theprincipal components representation allows mapping one or more signalrepresentations as a function of process parameters or geometricparameters over the entire wafer. The nature of the pattern captures theessential properties of the device, whether it includes isolated ordense features.

In block 205, an image based signal response metrology (SRM) model istrained based on the generated images, or features extracted from thegenerated images and the reference values of the at least one parameterof interest. The image-based SRM model is structured to receive imagedata generated by a metrology system at one or more measurement sites,and directly determine the parameter(s) of interest associated with eachmeasurement target. In some embodiments, the image-based measurementmodel is implemented as a neural network model. In one example, thenumber of nodes of the neural network is selected based on the featuresextracted from the image data. In other examples, the image-based SRMmodel may be implemented as a linear model, a polynomial model, aresponse surface model, a support vector machines model, or other typesof models. In some examples, the image-based measurement model may beimplemented as a combination of models. In some examples, the selectedmodel is trained based on the reduced set of signals determined from thefeature extraction model and the measured reference values of theparameter(s) of interest. The model is trained such that its output fitsthe measured reference values of the parameter(s) of interest for allthe images in the parameter variation space defined by the DOE images.

As depicted in FIG. 1, computing system 130 trains an image-based SRMmodel such that its output fits the reference values received fromreference measurement source 150 or reference values calculated based onmeasurement signals 121 for each DOE image of each DOE measurement sitereceived from field imaging detector 114 or pupil imaging detector 117.

In another aspect, the trained image based SRM model is employed as themeasurement model for measurement of other wafers. FIG. 3 illustrates amethod 210 suitable for implementation by a metrology system such asmetrology system 100 illustrated in FIG. 1 of the present invention. Inone aspect, it is recognized that data processing blocks of method 210may be carried out via a pre-programmed algorithm executed by one ormore processors of computing system 130, or any other general purposecomputing system. It is recognized herein that the particular structuralaspects of metrology system 100 do not represent limitations and shouldbe interpreted as illustrative only.

In block 211, a measurement site is illuminated in accordance with thesame image based metrology technique, or combination of image basedmetrology techniques employed to generate the images used to train theimage based SRM model. The measurement site is a different measurementsite than any of the DOE measurement sites. The measurement siteincludes an instance of at least one structure characterized by theparameter(s) of interest.

In block 212, light imaged from the measurement site is detected inresponse to the illuminating of the measurement site. In one example,field imaging detector 114 depicted in FIG. 1 detects light imaged fromthe surface of wafer 107 at the measurement site. In another example,pupil imaging detector 117 detects light imaged from the pupil ofobjective 106 at the measurement site.

In block 213, an image of the measurement site is generated. In oneexample, field imaging detector 114 generates an image of themeasurement site and communicates signals 122 indicative of thegenerated image to computing system 130. In another example, pupilimaging detector 117 generates a pupil image of the measurement site andcommunicates signals indicative of the generated pupil image tocomputing system 130.

In some examples, the image data is subjected to the same alignment,filtering, sampling, and feature extraction steps described withreference to method 200. Although, the use of any, or all, of thesesteps may be advantageous, in general, it is not necessary. In thissense, these steps are optional.

In block 214, the value of at least one parameter of interestcharacterizing the instance of the structure at the measurement site isdetermined based on the trained image based SRM model and the image ofthe measurement site. The image of measurement site is processed by theimage based SRM model to determine the value(s) of the parameter(s) ofinterest.

In another block (not shown), the determined value(s) of theparameter(s) of interest are stored in a memory. For example, theparameter values may be stored on-board the measurement system 100, forexample, in memory 132, or may be communicated (e.g., via output signal140) to an external memory device.

In another aspect, measured images are transformed into syntheticnon-imaging based measurement signals associated with a model-basedmeasurement technique at one or more locations a field. The model-basedmeasurement technique is employed to estimate values of structuralparameters of interest based on the synthetic signals. A measurementsignal synthesis model is trained based on measured, image-basedtraining data (e.g., images collected from a Design of Experiments (DOE)wafer) and corresponding non-imaging measurement data. In a furtheraspect, synthetic signals are generated for multiple structures indifferent locations in each imaged field. In some examples, performingmodel based measurements based on the synthetic signals is significantlyfaster than acquiring actual measurement data at each differentlocation.

FIG. 9 illustrates a method 220 suitable for implementation by ameasurement system such as measurement system 100 illustrated in FIG. 1of the present invention. In one aspect, it is recognized that dataprocessing blocks of method 200 may be carried out via a pre-programmedalgorithm executed by one or more processors of computing system 130, orany other general purpose computing system. It is recognized herein thatthe particular structural aspects of measurement system 100 do notrepresent limitations and should be interpreted as illustrative only.

In block 221, a plurality of DOE measurement sites are illuminated by anillumination source. The DOE measurement sites are located at a numberof different fields located on one or more DOE wafers. Each DOEmeasurement site includes an instance of at least one structurecharacterized by at least one parameter of interest. The structure canbe a dedicated metrology target, device structure, grating structure,etc.

As described with reference to method 200, the parameters of interestinclude one or more process parameters, structural parameters,dispersion parameters, or layout parameters. Each of the measurementsites includes the same nominal structures at the same nominal locationswithin each of the measurement sites. In one example, a measurement siteencompasses a field area of a semiconductor wafer that is repeatedlyconstructed across the wafer surface. In some examples, a measurementsite encompasses a die area that is repeatedly constructed across thewafer surface. Although, each measurement site nominally includes thesame structures, in reality, and for purposes of model training, eachmeasurement site includes variations of various parameters (e.g., CD,sidewall angle, height, overlay, etc.). For purposes of model training,variations of the parameter(s) of interest are organized in a Design ofExperiments (DOE) pattern on the surface of a semiconductor wafer (e.g.,DOE wafer) as described with reference to method 200.

In block 222, light imaged from each of the plurality of DOE measurementsites is detected in response to the illuminating of each of theplurality of DOE measurement sites. In one example, field imagingdetector 114 depicted in FIG. 1 detects light imaged from the surface ofwafer 107 at each DOE measurement site. In another example, pupilimaging detector 117 detects light imaged from the pupil of objective106 at each DOE measurement site.

In block 223, an image of each of the plurality of DOE measurement sitesis generated. In one example, field imaging detector 114 generates animage of each of the DOE measurement sites and communicates signals 122indicative of each generated image to computing system 130. In anotherexample, pupil imaging detector 117 generates a pupil image of each ofthe DOE measurement sites and communicates signals indicative of eachgenerated pupil image to computing system 130.

In some examples, a single image of each measurement site is generated.In these examples, each image of each measurement site includes a singlemeasurement signal value associated with each image pixel. In someexamples, the single measurement value is a reflectance at the locationof each pixel measured by an imaging reflectometer at a particular setof measurement system settings (e.g., wavelength, polarization, angle ofincidence, azimuth angle, etc.). In some other examples, multiple imagesof each measurement site are generated, as described with reference tomethod 200.

In some examples, the image data is subjected to the same alignment,filtering, sampling, and feature extraction steps described withreference to method 200. Although, the use of any, or all, of thesesteps may be advantageous, in general, it is not necessary. In thissense, these steps are optional.

In block 224, light collected from each parameterized structure locatedwithin each of the plurality of DOE measurement sites is detected inaccordance with one or more non-imaging measurement techniques. In oneexample depicted in FIG. 1, spectroscopic field detector 113 detectslight collected from one or more structures characterized by eachparameter of interest within each measurement site.

In block 225, one or more measurement signals indicative of the detectedlight at each of the plurality of DOE measurement sites is generated. Inone example, depicted in FIG. 1, spectroscopic field detector 113generates measurement signals 121 indicative of light collected from oneor more structures characterized by each parameter of interest withineach measurement site. In this example, the measurement signals arespectroscopic scatterometry signals.

In block 226, a measurement signal synthesis model is trained. Themeasurement signal synthesis model relates the images of each of theplurality of DOE measurement sites to the one or more sets ofmeasurement signals associated with each non-imaging measurement of eachparameterized structure at each of the plurality of DOE measurementsites. The measurement signal synthesis model is structured to receiveimage data generated by a metrology system at one or more measurementsites, and directly determine synthetic measurement signals associatedwith a non-imaging measurement of each parameterized structure locatedwithin each of the plurality of DOE measurement sites. In someembodiments, the measurement signal synthesis model is implemented as aneural network model. In one example, the number of nodes of the neuralnetwork is selected based on the features extracted from the image data.In other examples, the measurement signal synthesis model may beimplemented as a linear model, a polynomial model, a response surfacemodel, a support vector machines model, or other types of models. Insome examples, the measurement signal synthesis model may be implementedas a combination of models. In some examples, the selected model istrained based on the reduced set of signals determined from the featureextraction model and the signals measured based on one or morenon-imaging metrology techniques. The model is trained such that itsoutput fits the measured signals for all the images in the parametervariation space defined by the DOE images.

As depicted in FIG. 1, computing system 130 trains an measurement signalsynthesis model such that its output fits the measured signals 121associated each parameterized structure measured within each DOEmeasurement site for each image of each DOE measurement site receivedfrom field imaging detector 114 or pupil imaging detector 117.

In one further aspect, the trained measurement signal synthesis model isemployed to transform measured images into synthetic non-imaging basedmeasurement signals associated with one or more model-based measurementtechniques. The one or more model-based measurement techniques areemployed to estimate values of structural parameters of interest basedon the synthetic signals.

FIG. 10 illustrates a method 230 suitable for implementation by ametrology system such as metrology system 100 illustrated in FIG. 1 ofthe present invention. In one aspect, it is recognized that dataprocessing blocks of method 230 may be carried out via a pre-programmedalgorithm executed by one or more processors of computing system 130, orany other general purpose computing system. It is recognized herein thatthe particular structural aspects of metrology system 100 do notrepresent limitations and should be interpreted as illustrative only.

In block 231, a measurement site is illuminated in accordance with thesame image based metrology technique, or combination of image basedmetrology techniques employed to generate the images used to train themeasurement signal synthesis model. The measurement site is a differentmeasurement site than any of the DOE measurement sites. The measurementsite includes an instance of at least one structure characterized by theparameter(s) of interest.

In block 232, light imaged from the measurement site is detected inresponse to the illuminating of the measurement site. In one example,field imaging detector 114 depicted in FIG. 1 detects light imaged fromthe surface of wafer 107 at the measurement site. In another example,pupil imaging detector 117 detects light imaged from the pupil ofobjective 106 at the measurement site.

In block 233, an image of the measurement site is generated. In oneexample, field imaging detector 114 generates an image of themeasurement site and communicates signals 122 indicative of thegenerated image to computing system 130. In another example, pupilimaging detector 117 generates a pupil image of the measurement site andcommunicates signals indicative of the generated pupil image tocomputing system 130.

In some examples, the image data is subjected to the same alignment,filtering, sampling, and feature extraction steps described withreference to method 230. Although, the use of any, or all, of thesesteps may be advantageous, in general, it is not necessary. In thissense, these steps are optional.

In block 234, a set of synthetic measurement signals associated with themeasurement site is generated based on the trained measurement signalsynthesis model and the image of the measurement site. The syntheticmeasurement signals are associated with different instances of the samestructure(s) characterized by each parameter of interest within eachmeasurement site used for training of the measurement signal synthesismodel. In one example, the synthetic measurement signals are associatedwith a measurement performed by spectroscopic field detector 113depicted in FIG. 1.

In block 235, a value of at least one parameter of interestcharacterizing the instance of the at least one structure at themeasurement site is determined based on a fitting of the syntheticmeasurement signals to a model of a measurement of the measurement sitein accordance with the non-imaging measurement technique. In oneexample, the set of synthetic signals are received by computing system130. Computing system 130 performs a model based measurement (e.g.,optical critical dimension measurement) to estimate the value of eachparameter of interest at each measurement site based on the set ofsynthetic measurement signals.

In block 236, the determined value(s) of the parameter(s) of interestare stored in a memory. For example, the parameter values may be storedon-board the measurement system 100, for example, in memory 132, or maybe communicated (e.g., via output signal 140) to an external memorydevice.

In another further aspect, values of parameters of interest may bedetermined from images of on-device structures in accordance with thesystems and methods described herein. In some embodiments, images ofon-device structures are used to train an image-based SRM model or ameasurement signal synthesis model as described herein. The trainedmodels are then used to calculate values of one or more parameters ofinterest directly from images, or indirectly, via synthetic signals, ofthe same on-device structures collected from other wafers. In theseembodiments, the use of specialized targets is avoided. In some otherembodiments, metrology targets are used and the target size can be lessthan 10 microns by 10 microns. In general, if metrology targets areused, multiple targets can be measured from single image and themetrology target can include one structure or more than one differentstructure.

Exemplary structures characterized by parameters of interest measured inaccordance with the methods and systems described herein includeline-space grating structures, FinFET structures, SRAM memorystructures, FLASH memory structures, and DRAM memory structures.

As described hereinbefore, the measurement methods and systems describedherein are not constrained to specialized targets. In general, anytarget that exhibits sensitivity to a parameter of interest when imagedby the available imaging system may be employed in accordance with themethods and systems described herein. However, in some examples, it isadvantageous to employ specialized measurement targets that exhibit highsensitivity to a parameter of interest when imaged by the availableimaging system to enhance image-based measurement performance. In someexamples, measurement targets are located in the scribe lines of asemiconductor area. In other examples, the measurement targets arelocated within the device area.

In another further aspect, the methods and systems for training theimage-based measurement model include an optimization algorithm toautomate any or all of the elements required to arrive at a trainedmodel.

In some examples, an optimization algorithm is configured to maximizethe performance of the measurement (defined by a cost function) byoptimizing any or all of the following parameters: the list of imagefilters, the parameters of the filters, pixel sampling, the type offeature extraction model, the parameters of the selected featureextraction model, the type of measurement model, the parameters of theselected measurement model. The optimization algorithm can include userdefined heuristics and can be combination of nested optimizations (e.g.,combinatorial and continuous optimization).

In a further aspect, image data including multiple, different targets iscollected for model building, training, and measurement. The use ofimage data associated with multiple targets having different structure,but formed by the same process conditions increases the informationembedded in the model and reduces the correlation to process or otherparameter variations. In particular, the use of training data thatincludes images of multiple, different targets at one or moremeasurement sites enables more accurate estimation of values ofparameters of interest. In one example, different grating structures orfilm pads are utilized as targets for model training and measurement asdescribed herein.

In another further aspect, signals from multiple targets can beprocessed to reduce sensitivity to process variations and increasesensitivity to the parameters of interest. In some examples, signalsfrom images, or portions of images, of different targets are subtractedfrom one another. In some other examples, signals from images, orportions of images, of different targets are fit to a model, and theresiduals are used to build, train, and use the models as describedherein. In one example, image signals from two different targets aresubtracted to eliminate, or significantly reduce, the effect of processnoise in each measurement result. In another example, measurementsignals from multiple targets are subtracted to eliminate, orsignificantly reduce, the effect of under layers in each measurementresult. The use of measurement data associated with multiple targetsincreases the sample and process information embedded in the model. Ingeneral, various mathematical operations can be applied between thesignals from different target images, or portions of target images todetermine image signals with reduced sensitivity to process variationsand increased sensitivity to the parameters of interest.

In another further aspect, measurement data derived from measurementsperformed by a combination of multiple, different measurement techniquesis collected for model building, training, and measurement. The use ofmeasurement data associated with multiple, different measurementtechniques increases the information content in the combined set ofsignals and reduces the correlation to process or other parametersvariations. Different measurement sites may be measured by multiple,different measurement techniques to enhance the measurement informationavailable for estimation of parameters of interest.

In another further aspect, measurement results at multiple wavelengthsare combined for model training and measurement in accordance with themethods and systems described herein.

In general, any image based measurement technique, or combination of twoor more image based measurement techniques may be contemplated withinthe scope of this patent document as the data processed by any of thefeature extraction model, the image-based SRM model, and the measurementsignal synthesis model is in vector form. Because the models operate onvectors of data, each pixel of image data is treated independently. Inaddition, it is possible to concatenate data from multiple, differentmetrologies, regardless of whether the data is two dimensional imagedata, one dimensional image data, or even single point data.

Exemplary measurement techniques that may be applied to providemeasurement signals for model training and measurement models forparameter estimation in accordance with the methods described hereininclude, but are not limited to spectroscopic ellipsometry at one ormore angles of illumination, including Mueller matrix ellipsometry,spectroscopic reflectometry, angle resolve reflectometry, spectroscopicscatterometry, scatterometry overlay, beam profile reflectometry, bothangle-resolved and polarization-resolved, beam profile ellipsometry,single or multiple discrete wavelength ellipsometry, single wavelengthreflectometry, single wavelength ellipsometry, transmission small anglex-ray scatterometer (TSAXS), small angle x-ray scattering (SAXS),grazing incidence small angle x-ray scattering (GISAXS), wide anglex-ray scattering (WAXS), x-ray reflectivity (XRR), x-ray diffraction(XRD), grazing incidence x-ray diffraction (GIXRD), high resolutionx-ray diffraction (HRXRD), x-ray photoelectron spectroscopy (XPS), x-rayfluorescence (XRF), grazing incidence x-ray fluorescence (GIXRF), x-raytomography, x-ray ellipsometry, scanning electron microscopy, tunnelingelectron microscopy, and atomic force microscopy. Any of theaforementioned metrology techniques may be implemented separately aspart of a stand-alone measurement system, or combined into an integratedmeasurement system. In general, measurement data collected by differentmeasurement technologies and analyzed in accordance with the methodsdescribed herein may be collected from multiple tools, rather than onetool integrating multiple technologies.

In another further aspect, signals measured by multiple metrologies canbe processed to reduce sensitivity to process variations and increasesensitivity to the parameters of interest. In some examples, signalsfrom images, or portions of images, of targets measured by differentmetrologies are subtracted from one another. In some other examples,signals from images, or portions of images, of targets measured bydifferent metrologies are fit to a model, and the residuals are used tobuild, train, and use the image-based measurement model as describedherein. In one example, image signals from a target measured by twodifferent metrologies are subtracted to eliminate, or significantlyreduce, the effect of process noise in each measurement result. Ingeneral, various mathematical operations can be applied between thesignals of target images, or portions of target images, measured bydifferent metrologies to determine image signals with reducedsensitivity to process variations and increased sensitivity to theparameters of interest.

In general, image signals from multiple targets each measured bymultiple metrology techniques increases the information content in thecombined set of signals and reduces the overlay correlation to processor other parameters variations.

In another further aspect, image data and non-imaging data may becollected from measurement targets such as dedicated metrology targets,device structures, or proxy structures found within the fields or dieareas on the wafer, or within scribe lines.

In some examples, the measurement methods described herein areimplemented as an element of a SpectraShape® optical critical-dimensionmetrology system available from KLA-Tencor Corporation, Milpitas,Calif., USA.

In some other examples, the measurement methods described herein areimplemented off-line, for example, by a computing system implementingAcuShape® software available from KLA-Tencor Corporation, Milpitas,Calif., USA.

In yet another aspect, the measurement results described herein can beused to provide active feedback to a process tool (e.g., lithographytool, etch tool, deposition tool, etc.). For example, values of overlayerror determined using the methods described herein can be communicatedto a lithography tool to adjust the lithography system to achieve adesired output. In a similar way etch parameters (e.g., etch time,diffusivity, etc.) or deposition parameters (e.g., time, concentration,etc.) may be included in a measurement model to provide active feedbackto etch tools or deposition tools, respectively.

In general, the systems and methods described herein can be implementedas part of the process of preparing a measurement model for off-line oron-tool measurement. In addition, the measurement model may describe oneor more target structures, device structures, and measurement sites.

In a further aspect, an image utilized for model training andmeasurement as described herein is a result of a linear or non-lineartransformation from multiple partial images of different locations inthe field.

In another further aspect, an image utilized for model training andmeasurement as described herein is a result of a linear or non-lineartransformation from multiple partial images of different locations indifferent fields.

In another further aspect an image utilized for model training andmeasurement as described herein is a result of a linear or non-lineartransformation from multiple partial images of different locations in afield and non-imaging measurement signals (e.g., scatterometry signals)used for model training are associated with different measurementlocations in the field.

In a further embodiment, system 100 may include one or more computingsystems 130 employed to perform measurements in accordance with themethods described herein. The one or more computing systems 130 may becommunicatively coupled to the detectors of system 100. In one aspect,the one or more computing systems 130 are configured to receivemeasurement data associated with measurements of the structure ofspecimen 107.

It should be recognized that the various steps described throughout thepresent disclosure may be carried out by a single computer system 130or, alternatively, a multiple computer system 130. Moreover, differentsubsystems of the system 100, such as the scatterometer and the beamprofile reflectometer, may include a computer system suitable forcarrying out at least a portion of the steps described herein.Therefore, the aforementioned description should not be interpreted as alimitation on the present invention but merely an illustration. Further,the one or more computing systems 130 may be configured to perform anyother step(s) of any of the method embodiments described herein.

In addition, the computer system 130 may be communicatively coupled tothe detectors of system 100 in any manner known in the art. For example,the one or more computing systems 130 may be coupled to computingsystems associated with the detectors of system 100. In another example,the detectors may be controlled directly by a single computer systemcoupled to computer system 130.

The computer system 130 of the metrology system 100 may be configured toreceive and/or acquire data or information from the subsystems of thesystem (e.g., detectors 113, 114, and 117, and the like) by atransmission medium that may include wireline and/or wireless portions.In this manner, the transmission medium may serve as a data link betweenthe computer system 130 and other subsystems of the system 100.

Computer system 130 of system 300 may be configured to receive and/oracquire data or information (e.g., measurement results, modeling inputs,modeling results, etc.) from other systems by a transmission medium thatmay include wireline and/or wireless portions. In this manner, thetransmission medium may serve as a data link between the computer system130 and other systems (e.g., memory on-board metrology system 100,external memory, or other external systems). For example, the computingsystem 130 may be configured to receive measurement data from a storagemedium (i.e., memory 132 or an external memory) via a data link. Forinstance, spectral measurement results obtained using spectrometer 113may be stored in a permanent or semi-permanent memory device (e.g.,memory 132 or an external memory). In this regard, the spectral resultsmay be imported from on-board memory or from an external memory system.Moreover, the computer system 130 may send data to other systems via atransmission medium. For instance, a parameter value 140 determined bycomputer system 130 may be communicated and stored in an externalmemory. In this regard, measurement results may be exported to anothersystem.

Computing system 130 may include, but is not limited to, a personalcomputer system, mainframe computer system, workstation, image computer,parallel processor, or any other device known in the art. In general,the term “computing system” may be broadly defined to encompass anydevice having one or more processors, which execute instructions from amemory medium.

Program instructions 134 implementing methods such as those describedherein may be transmitted over a transmission medium such as a wire,cable, or wireless transmission link. For example, as illustrated inFIG. 1, program instructions 134 stored in memory 132 are transmitted toprocessor 131 over bus 133. Program instructions 134 are stored in acomputer readable medium (e.g., memory 132). Exemplary computer-readablemedia include read-only memory, a random access memory, a magnetic oroptical disk, or a magnetic tape.

As described herein, the term “critical dimension” includes any criticaldimension of a structure (e.g., bottom critical dimension, middlecritical dimension, top critical dimension, sidewall angle, gratingheight, etc.), a critical dimension between any two or more structures(e.g., distance between two structures), and a displacement between twoor more structures (e.g., overlay displacement between overlayinggrating structures, etc.). Structures may include three dimensionalstructures, patterned structures, overlay structures, etc.

As described herein, the term “critical dimension application” or“critical dimension measurement application” includes any criticaldimension measurement.

As described herein, the term “metrology system” includes any systememployed at least in part to characterize a specimen in any aspect,including measurement applications such as critical dimension metrology,overlay metrology, focus/dosage metrology, and composition metrology.However, such terms of art do not limit the scope of the term “metrologysystem” as described herein. In addition, the metrology system 100 maybe configured for measurement of patterned wafers and/or unpatternedwafers. The metrology system may be configured as a LED inspection tool,edge inspection tool, backside inspection tool, macro-inspection tool,or multi-mode inspection tool (involving data from one or more platformssimultaneously), and any other metrology or inspection tool thatbenefits from the calibration of system parameters based on criticaldimension data.

Various embodiments are described herein for a semiconductor processingsystem (e.g., an inspection system or a lithography system) that may beused for processing a specimen. The term “specimen” is used herein torefer to a wafer, a reticle, or any other sample that may be processed(e.g., printed or inspected for defects) by means known in the art.

As used herein, the term “wafer” generally refers to substrates formedof a semiconductor or non-semiconductor material. Examples include, butare not limited to, monocrystalline silicon, gallium arsenide, andindium phosphide. Such substrates may be commonly found and/or processedin semiconductor fabrication facilities. In some cases, a wafer mayinclude only the substrate (i.e., bare wafer). Alternatively, a wafermay include one or more layers of different materials formed upon asubstrate. One or more layers formed on a wafer may be “patterned” or“unpatterned.” For example, a wafer may include a plurality of dieshaving repeatable pattern features.

A “reticle” may be a reticle at any stage of a reticle fabricationprocess, or a completed reticle that may or may not be released for usein a semiconductor fabrication facility. A reticle, or a “mask,” isgenerally defined as a substantially transparent substrate havingsubstantially opaque regions formed thereon and configured in a pattern.The substrate may include, for example, a glass material such asamorphous SiO₂. A reticle may be disposed above a resist-covered waferduring an exposure step of a lithography process such that the patternon the reticle may be transferred to the resist.

One or more layers formed on a wafer may be patterned or unpatterned.For example, a wafer may include a plurality of dies, each havingrepeatable pattern features. Formation and processing of such layers ofmaterial may ultimately result in completed devices. Many differenttypes of devices may be formed on a wafer, and the term wafer as usedherein is intended to encompass a wafer on which any type of deviceknown in the art is being fabricated.

In one or more exemplary embodiments, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage media may be any available media that can be accessed by ageneral purpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code means in the form of instructions or datastructures and that can be accessed by a general-purpose orspecial-purpose computer, or a general-purpose or special-purposeprocessor. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, includes compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Combinations of the above should also beincluded within the scope of computer-readable media.

Although certain specific embodiments are described above forinstructional purposes, the teachings of this patent document havegeneral applicability and are not limited to the specific embodimentsdescribed above. Accordingly, various modifications, adaptations, andcombinations of various features of the described embodiments can bepracticed without departing from the scope of the invention as set forthin the claims.

What is claimed is:
 1. A system comprising: at least one illuminationsource configured to illuminate a plurality of Design Of Experiments(DOE) measurement sites located at a plurality of fields of one or moreDOE wafers, wherein each DOE measurement site includes an instance of atleast one structure characterized by at least one parameter of interest;at least one imaging detector configured to detect light imaged fromeach of the plurality of DOE measurement sites and generate one or moreimages of each of the plurality of DOE measurement sites; a referencemeasurement system configured to estimate a reference value of the atleast one parameter of interest at each of the plurality of DOEmeasurement sites; and a computing system configured to: receive the oneor more images of each of the plurality of DOE measurement sites and thereference value of the at least one parameter of interest at each of theplurality of DOE measurement sites; select a subset of pixels associatedwith each of the one or more images; and train an image based signalresponse metrology (SRM) model that relates an image of a measurementsite including an instance of the at least one structure to a value ofthe at least one parameter of interest of the at least one structurebased on the selected subset of pixels associated with each of the oneor more images of each of the plurality of DOE measurement sites and thecorresponding reference value of the at least one parameter of interestat each of the plurality of DOE measurement sites, the one or moreimages of the measurement site each comprising a plurality of pixelseach associated with a different position on the one or more DOE wafersand a single measured signal value associated with each pixel.
 2. Thesystem of claim 1, wherein the illumination source is further configuredto illuminate a measurement site different from any of the DOEmeasurement sites, wherein the measurement site includes an instance ofthe at least one structure characterized by the at least one parameterof interest; wherein the imaging detector is further configured todetect light imaged from the measurement site and generate one or moreimages of the measurement site indicative of the detected light, whereinthe computing system is further configured to: receive the one or moreimages of the measurement site; determine a value of the at least oneparameter of interest characterizing the instance of the at least onestructure at the measurement site based on the trained image based SRMmodel and the one or more images of the measurement site; and store thevalue of the at least one parameter of interest in a memory.
 3. Thesystem of claim 2, wherein a measurement signal value is associated witheach pixel of each image of each of the plurality of DOE measurementsites and the measurement site, and wherein the one or more images ofthe measurement site are derived from measurements performed by the samemeasurement technique or combination of measurement techniques at eachimage of each of the plurality of DOE measurement sites.
 4. The systemof claim 2, wherein the computing system is further configured to:extract features from each image of each of the plurality of DOEmeasurement sites with a feature extraction model that reduces adimension of each of the images, wherein the training of the image basedSRM model is based on the features extracted from each image of each ofthe plurality of DOE measurement sites and the corresponding referencevalue of the at least one parameter of interest at each of the pluralityof DOE measurement sites; and extract features from the one or moreimages of the measurement site with the feature extraction model,wherein the determining of the value of the at least one parameter ofinterest is based on the trained image based SRM model and the featuresextracted from the one or more images of the measurement site.
 5. Thesystem of claim 4, wherein the feature extraction model is any of aprincipal component analysis (PCA) model, an independent componentanalysis (ICA) model, a kernel PCA model, a non-linear PCA model, a fastFourier transform (FFT) model, a discrete cosine transform (DCT) model,and a wavelet model.
 6. The system of claim 1, wherein the plurality ofDesign Of Experiments (DOE) measurement sites includes a variation invalue of the at least one parameter of interest.
 7. The system of claim1, wherein the at least one parameter of interest is any of a criticaldimension (CD) parameter, an overlay parameter, a focus parameter, adose parameter, a structure asymmetry parameter, a structure roughnessparameter, a directed self assembly (DSA) pattern uniformity parameter,and a pitch walk parameter.
 8. The system of claim 1, wherein each DOEmeasurement site includes any of a metrology target, a periodic gratingstructure, and a device structure.
 9. The system of claim 1, wherein theat least one structure is any of a line-space grating structure, aFinFET structure, a SRAM memory structure, a FLASH memory structure, anda DRAM memory structure.
 10. The system of claim 1, wherein thereference measurement system is any of a scanning electron microscope,an optical based measurement system, an x-ray based measurement system,a tunneling electron microscopy system, and an atomic force microscopysystem.
 11. The system of claim 1, wherein the at least one parameter ofinterest is any of a process parameter value, a structural parametervalue, a dispersion parameter value, and a layout parameter value. 12.The system of claim 1, wherein the image based SRM model is any of alinear model, a polynomial model, a neural network model, a supportvector machines model, a decision tree model, and a random forest model.13. The system of claim 1, wherein the one or more images of each of theplurality of DOE measurement sites includes a combination of imagesacquired by two or more different metrology techniques.
 14. The systemof claim 1, wherein the selecting of the subset of pixels associatedwith each of the one or more images is based on proximity to the atleast one structure characterized by the at least one parameter ofinterest.
 15. The system of claim 1, wherein the selecting of the subsetof pixels associated with each of the one or more images is based on avariance of measurement signal values of the images of the plurality ofDOE measurement sites associated with each pixel location.
 16. A methodcomprising: illuminating a plurality of Design Of Experiments (DOE)measurement sites located at a plurality of fields of one or more DOEwafers, wherein each DOE measurement site includes an instance of atleast one structure characterized by at least one parameter of interest;detecting light imaged from each of the plurality of DOE measurementsites in response to the illuminating of each of the plurality of DOEmeasurement sites; generating an image of each of the plurality of DOEmeasurement sites; estimating a reference measurement value of the atleast one parameter of interest at each of the plurality of DOEmeasurement sites; selecting a subset of pixels associated with theimage of each of the plurality of DOE measurement sites; and training animage based signal response metrology (SRM) model based on the selectedsubset of pixels associated with the image of each of the plurality ofDOE measurement sites and the corresponding reference value of the atleast one parameter of interest at each of the plurality of DOEmeasurement sites, wherein the image based SRM model relates an image ofa measurement site including an instance of the at least one structureto a value of the at least one parameter of interest of the at least onestructure, the image of each of the plurality of measurement sitescomprising a plurality of pixels each associated with a differentposition on the one or more DOE wafers and a single measured signalvalue associated with each pixel.
 17. The method of claim 16, furthercomprising: illuminating a measurement site different from any of theDOE measurement sites, wherein the measurement site includes an instanceof the at least one structure characterized by the at least oneparameter of interest; detecting light imaged from the measurement sitein response to the illuminating of the measurement site; generating animage of the measurement site indicative of the detected light; anddetermining a value of the at least one parameter of interestcharacterizing the instance of the at least one structure at themeasurement site based on the trained image based SRM model and theimage of the measurement site.
 18. The method of claim 17, wherein ameasurement signal value is associated with each pixel of each image ofeach of the plurality of DOE measurement sites and the measurement site,and wherein the image of the measurement site is derived frommeasurements performed by the same measurement technique or combinationof measurement techniques as each image of each of the plurality of DOEmeasurement sites.
 19. The method of claim 17, further comprising:extracting features from each image of each of the plurality of DOEmeasurement sites with a feature extraction model that reduces adimension of each of the images, wherein the training of the image basedSRM model is based on the features extracted from each image of each ofthe plurality of DOE measurement sites and the corresponding referencevalue of the at least one parameter of interest at each of the pluralityof DOE measurement sites; and extracting features from the image of themeasurement site with the feature extraction model, wherein thedetermining of the value of the at least one parameter of interest isbased on the trained image based SRM model and the features extractedfrom the image of the measurement site.